Overview

Dataset statistics

Number of variables39
Number of observations10324
Missing cells0
Missing cells (%)0.0%
Duplicate rows104
Duplicate rows (%)1.0%
Total size in memory3.1 MiB
Average record size in memory312.0 B

Variable types

Numeric8
Categorical31

Alerts

Dataset has 104 (1.0%) duplicate rowsDuplicates
Line Item Quantity is highly correlated with Line Item Value and 3 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Pack Price is highly correlated with Unit Price and 1 other fieldsHigh correlation
Unit Price is highly correlated with Pack Price and 3 other fieldsHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Unit Price and 4 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Unit Price and 4 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Pack Price and 5 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 2 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Pack Price is highly correlated with Unit PriceHigh correlation
Unit Price is highly correlated with Pack PriceHigh correlation
Weight (Kilograms) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Freight Cost (USD) is highly correlated with Weight (Kilograms)High correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 2 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 5 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 2 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Product Group_ARV is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
Sub Classification_Adult is highly correlated with Product Group_ARV and 3 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Vendor INCO Term_EXW and 3 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTM and 1 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Sub Classification_Adult is highly correlated with Product Group_HRDT and 3 other fieldsHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Shipment Mode_Truck is highly correlated with Shipment Mode_AirHigh correlation
Sub Classification_ACT is highly correlated with Product Group_ACTHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
Product Group_ACT is highly correlated with Sub Classification_ACTHigh correlation
Product Group_HRDT is highly correlated with Sub Classification_Adult and 3 other fieldsHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Sub Classification_Malaria is highly correlated with Product Group_ANTMHigh correlation
Shipment Mode_Air is highly correlated with Shipment Mode_TruckHigh correlation
Product Group_ARV is highly correlated with Sub Classification_Adult and 3 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Sub Classification_AdultHigh correlation
Vendor INCO Term_EXW is highly correlated with Product Group_HRDT and 5 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Vendor INCO Term_EXW and 2 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Vendor INCO Term_EXW and 2 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Sub Classification_Adult and 3 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Sub Classification_MalariaHigh correlation
Fulfill Via_Direct Drop is highly correlated with Vendor INCO Term_EXW and 2 other fieldsHigh correlation
Unit of Measure (Per Pack) is highly correlated with Product Group_ACT and 4 other fieldsHigh correlation
Line Item Quantity is highly correlated with Line Item Value and 1 other fieldsHigh correlation
Line Item Value is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Line Item Insurance (USD) is highly correlated with Line Item Quantity and 1 other fieldsHigh correlation
Managed By_Ethiopia Field Office is highly correlated with Managed By_Haiti Field OfficeHigh correlation
Managed By_Haiti Field Office is highly correlated with Managed By_Ethiopia Field OfficeHigh correlation
Managed By_PMO - US is highly correlated with Managed By_South Africa Field OfficeHigh correlation
Managed By_South Africa Field Office is highly correlated with Managed By_PMO - USHigh correlation
Fulfill Via_Direct Drop is highly correlated with Fulfill Via_From RDC and 6 other fieldsHigh correlation
Fulfill Via_From RDC is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Vendor INCO Term_DDP is highly correlated with Fulfill Via_Direct Drop and 3 other fieldsHigh correlation
Vendor INCO Term_EXW is highly correlated with Fulfill Via_Direct Drop and 8 other fieldsHigh correlation
Vendor INCO Term_N/A - From RDC is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Shipment Mode_Air is highly correlated with Vendor INCO Term_DDP and 2 other fieldsHigh correlation
Shipment Mode_Truck is highly correlated with Vendor INCO Term_EXW and 1 other fieldsHigh correlation
Product Group_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ANTM is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Product Group_ARV is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Product Group_HRDT is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Product Group_MRDT is highly correlated with Sub Classification_MalariaHigh correlation
Sub Classification_ACT is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
Sub Classification_Adult is highly correlated with Vendor INCO Term_EXW and 4 other fieldsHigh correlation
Sub Classification_HIV test is highly correlated with Fulfill Via_Direct Drop and 6 other fieldsHigh correlation
Sub Classification_Malaria is highly correlated with Unit of Measure (Per Pack) and 2 other fieldsHigh correlation
Sub Classification_Pediatric is highly correlated with Unit of Measure (Per Pack) and 1 other fieldsHigh correlation
First Line Designation_No is highly correlated with First Line Designation_YesHigh correlation
First Line Designation_Yes is highly correlated with First Line Designation_NoHigh correlation
Unit Price is highly skewed (γ1 = 40.58598947) Skewed
Weight (Kilograms) is highly skewed (γ1 = 34.05916463) Skewed

Reproduction

Analysis started2022-05-21 11:59:29.688515
Analysis finished2022-05-21 11:59:49.413232
Duration19.72 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unit of Measure (Per Pack)
Real number (ℝ)

HIGH CORRELATION

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.943316949 × 10-17
Minimum-1.005417359
Maximum12.0404362
Zeros0
Zeros (%)0.0%
Negative7526
Negative (%)72.9%
Memory size80.8 KiB
2022-05-21T17:29:49.573802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.005417359
5-th percentile-0.7572980217
Q1-0.6267088971
median-0.234941523
Q30.156825851
95-th percentile2.115662721
Maximum12.0404362
Range13.04585356
Interquartile range (IQR)0.7835347481

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)3.397691963 × 1016
Kurtosis36.09399876
Mean2.943316949 × 10-17
Median Absolute Deviation (MAD)0.391767374
Skewness4.302502487
Sum0
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:49.815118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
-0.2349415234121
39.9%
-0.62670889712630
25.5%
0.2874149757976
 
9.5%
2.115662721670
 
6.5%
0.548593225474
 
4.6%
-0.7572980217470
 
4.6%
0.156825851222
 
2.2%
2.899197469157
 
1.5%
-1.005417359126
 
1.2%
-0.6920034594114
 
1.1%
Other values (21)364
 
3.5%
ValueCountFrequency (%)
-1.005417359126
 
1.2%
-0.99235844624
 
< 0.1%
-0.97929953378
 
0.1%
-0.95318170884
 
< 0.1%
-0.86176932152
 
< 0.1%
-0.78341584674
 
< 0.1%
-0.7572980217470
 
4.6%
-0.70506237192
 
< 0.1%
-0.6920034594114
 
1.1%
-0.62670889712630
25.5%
ValueCountFrequency (%)
12.040436216
 
0.2%
8.3839407065
 
< 0.1%
6.0333364617
 
0.1%
3.36931831839
 
0.4%
2.899197469157
 
1.5%
2.50743009553
 
0.5%
2.115662721670
6.5%
1.59330622276
 
0.7%
1.33212797376
 
0.7%
1.1754210233
 
< 0.1%

Line Item Quantity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.607138961 × 10-17
Minimum-0.4579064323
Maximum15.02912585
Zeros0
Zeros (%)0.0%
Negative7833
Negative (%)75.9%
Memory size80.8 KiB
2022-05-21T17:29:50.063489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4579064323
5-th percentile-0.4575067657
Q1-0.4477399122
median-0.3829939168
Q3-0.03229268645
95-th percentile1.813962354
Maximum15.02912585
Range15.48703228
Interquartile range (IQR)0.4154472258

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)1.513587712 × 1016
Kurtosis40.0503001
Mean6.607138961 × 10-17
Median Absolute Deviation (MAD)0.07368853644
Skewness5.038314699
Sum6.536993169 × 10-13
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:50.196134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.208139762593
 
0.9%
-0.432952246691
 
0.9%
-0.45543349587
 
0.8%
-0.407973081773
 
0.7%
-0.33303558769
 
0.7%
-0.44544182967
 
0.6%
0.0416518864467
 
0.6%
-0.382993916866
 
0.6%
-0.45785647463
 
0.6%
0.791026833362
 
0.6%
Other values (5055)9586
92.9%
ValueCountFrequency (%)
-0.457906432335
0.3%
-0.457881453240
0.4%
-0.45785647463
0.6%
-0.457831494846
0.4%
-0.457806515728
0.3%
-0.457781536548
0.5%
-0.457756557327
0.3%
-0.457731578226
0.3%
-0.45770659922
 
0.2%
-0.457681619854
0.5%
ValueCountFrequency (%)
15.029125851
 
< 0.1%
14.552198651
 
< 0.1%
13.4104261
 
< 0.1%
12.406338513
< 0.1%
12.394498391
 
< 0.1%
11.033508591
 
< 0.1%
10.532901141
 
< 0.1%
10.493159291
 
< 0.1%
9.5827186891
 
< 0.1%
9.5337345472
< 0.1%

Line Item Value
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8754
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.469490233 × 10-17
Minimum-0.4566811858
Maximum16.78221805
Zeros0
Zeros (%)0.0%
Negative7651
Negative (%)74.1%
Memory size80.8 KiB
2022-05-21T17:29:50.336762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4566811858
5-th percentile-0.4560855913
Q1-0.444015585
median-0.3683149727
Q30.02540381669
95-th percentile1.57894586
Maximum16.78221805
Range17.23889923
Interquartile range (IQR)0.4694194017

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)-1.545791706 × 1016
Kurtosis54.15746236
Mean-6.469490233 × 10-17
Median Absolute Deviation (MAD)0.08674285175
Skewness5.837353819
Sum-5.684341886 × 10-13
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:50.470402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.122583807729
 
0.3%
-0.410340012923
 
0.2%
-0.454364154618
 
0.2%
-0.414974133116
 
0.2%
0.250647718815
 
0.1%
-0.447412974415
 
0.1%
-0.109122201313
 
0.1%
-0.455957133411
 
0.1%
-0.453437330611
 
0.1%
-0.340828210211
 
0.1%
Other values (8744)10162
98.4%
ValueCountFrequency (%)
-0.45668118581
< 0.1%
-0.45668112781
< 0.1%
-0.45668086721
< 0.1%
-0.45668063551
< 0.1%
-0.45668051961
< 0.1%
-0.45668049061
< 0.1%
-0.45667999831
< 0.1%
-0.45667976661
< 0.1%
-0.45667918731
< 0.1%
-0.45667878181
< 0.1%
ValueCountFrequency (%)
16.782218051
< 0.1%
16.251342511
< 0.1%
14.980416511
< 0.1%
14.430762191
< 0.1%
13.906895871
< 0.1%
11.936322751
< 0.1%
11.790805231
< 0.1%
11.169167791
< 0.1%
10.934217891
< 0.1%
10.850572022
< 0.1%

Pack Price
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct1185
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.752974567 × 10-17
Minimum-0.4808215048
Maximum29.02243684
Zeros0
Zeros (%)0.0%
Negative7591
Negative (%)73.5%
Memory size80.8 KiB
2022-05-21T17:29:50.612021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4808215048
5-th percentile-0.4393828045
Q1-0.3900510184
median-0.2771360415
Q30.03792963217
95-th percentile1.272978303
Maximum29.02243684
Range29.50325834
Interquartile range (IQR)0.4279806506

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)3.632610509 × 1016
Kurtosis293.0774112
Mean2.752974567 × 10-17
Median Absolute Deviation (MAD)0.1440488153
Skewness12.98494768
Sum1.98951966 × 10-13
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:50.743670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2205668668368
 
3.6%
1.272978303307
 
3.0%
1.470305447183
 
1.8%
-0.235039584139
 
1.3%
-0.0425359922110
 
1.1%
-0.288975670191
 
0.9%
-0.438286542691
 
0.9%
-0.427543175890
 
0.9%
-0.434997756889
 
0.9%
-0.431489718788
 
0.9%
Other values (1175)8768
84.9%
ValueCountFrequency (%)
-0.480821504886
0.8%
-0.47248991432
 
< 0.1%
-0.46569309042
 
< 0.1%
-0.46130804274
 
< 0.1%
-0.45725187374
 
< 0.1%
-0.45692299512
 
< 0.1%
-0.45604598562
 
< 0.1%
-0.45538822841
 
< 0.1%
-0.45473047131
 
< 0.1%
-0.45451121891
 
< 0.1%
ValueCountFrequency (%)
29.022436841
 
< 0.1%
26.925507051
 
< 0.1%
26.761725523
 
< 0.1%
15.969246251
 
< 0.1%
14.866626021
 
< 0.1%
8.2890545429
 
0.1%
7.1927926339
0.4%
6.2756599143
 
< 0.1%
6.2473763563
 
< 0.1%
6.1300763321
 
< 0.1%

Unit Price
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct195
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.101189827 × 10-17
Minimum-0.1842192611
Maximum72.67010008
Zeros0
Zeros (%)0.0%
Negative8104
Negative (%)78.5%
Memory size80.8 KiB
2022-05-21T17:29:50.884297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1842192611
5-th percentile-0.1839902939
Q1-0.1628489865
median-0.1384258154
Q3-0.04378602742
95-th percentile0.3011912641
Maximum72.67010008
Range72.85431934
Interquartile range (IQR)0.119062959

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)-9.081526273 × 1016
Kurtosis2726.111992
Mean-1.101189827 × 10-17
Median Absolute Deviation (MAD)0.03663475662
Skewness40.58598947
Sum5.684341886 × 10-14
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:51.013950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.175060572720
 
7.0%
-0.1842192611517
 
5.0%
-0.1506374009464
 
4.5%
-0.1445316081445
 
4.3%
0.05695955329411
 
4.0%
-0.1536902973400
 
3.9%
0.3011912641368
 
3.6%
-0.1720076756343
 
3.3%
-0.1384258154343
 
3.3%
-0.1292671262321
 
3.1%
Other values (185)5992
58.0%
ValueCountFrequency (%)
-0.1842192611517
5.0%
-0.1826928134
 
< 0.1%
-0.1811663648152
 
1.5%
-0.179639916611
 
0.1%
-0.1781134684253
 
2.5%
-0.17658702021
 
< 0.1%
-0.175060572720
7.0%
-0.1720076756343
3.3%
-0.17048122741
 
< 0.1%
-0.1689547792278
 
2.7%
ValueCountFrequency (%)
72.670100081
 
< 0.1%
12.537199981
 
< 0.1%
11.261089292
 
< 0.1%
8.9714169981
 
< 0.1%
8.0280720151
 
< 0.1%
7.4449688064
 
< 0.1%
7.399175363
 
< 0.1%
7.29232398746
0.4%
6.83438952923
0.2%
5.0392864543
 
< 0.1%

Weight (Kilograms)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct3960
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.30356948 × 10-17
Minimum-0.3409917532
Maximum68.18708781
Zeros0
Zeros (%)0.0%
Negative7749
Negative (%)75.1%
Memory size80.8 KiB
2022-05-21T17:29:51.152575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.3409917532
5-th percentile-0.3393072321
Q1-0.3238068398
median-0.2462149573
Q3-0.001130115975
95-th percentile1.051757559
Maximum68.18708781
Range68.52807956
Interquartile range (IQR)0.3226767238

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)-3.027175424 × 1016
Kurtosis2135.81298
Mean-3.30356948 × 10-17
Median Absolute Deviation (MAD)0.08964130437
Skewness34.05916463
Sum-3.801403636 × 10-13
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:51.282233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.340911823455
 
0.5%
-0.337394910948
 
0.5%
0.521011498740
 
0.4%
-0.332119542132
 
0.3%
-0.336755472231
 
0.3%
-0.337954419731
 
0.3%
-0.340592104131
 
0.3%
-0.338833647830
 
0.3%
-0.340112525126
 
0.3%
-0.340352314626
 
0.3%
Other values (3950)9974
96.6%
ValueCountFrequency (%)
-0.340991753223
0.2%
-0.34095178831
 
< 0.1%
-0.340911823455
0.5%
-0.34087185854
 
< 0.1%
-0.340831893620
 
0.2%
-0.340751963719
 
0.2%
-0.34071199882
 
< 0.1%
-0.340672033921
 
0.2%
-0.3406320692
 
< 0.1%
-0.340592104131
0.3%
ValueCountFrequency (%)
68.187087811
 
< 0.1%
22.926182121
 
< 0.1%
16.084748181
 
< 0.1%
12.030467411
 
< 0.1%
8.6132273853
< 0.1%
6.8882617232
 
< 0.1%
6.753579965
< 0.1%
6.7079400271
 
< 0.1%
6.6188981962
 
< 0.1%
6.4631948871
 
< 0.1%

Freight Cost (USD)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct6628
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.30356948 × 10-17
Minimum-0.6946675877
Maximum16.21874812
Zeros0
Zeros (%)0.0%
Negative6980
Negative (%)67.6%
Memory size80.8 KiB
2022-05-21T17:29:51.420861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.6946675877
5-th percentile-0.6530372882
Q1-0.5576140228
median-0.3226243468
Q30.1608384717
95-th percentile1.709377804
Maximum16.21874812
Range16.91341571
Interquartile range (IQR)0.7184524945

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)-3.027175424 × 1016
Kurtosis30.37207566
Mean-3.30356948 × 10-17
Median Absolute Deviation (MAD)0.2838259114
Skewness4.337672543
Sum-5.258016245 × 10-13
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:51.549516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.126200027337
 
0.4%
-0.259935460929
 
0.3%
-0.335764613727
 
0.3%
-0.611314028425
 
0.2%
-0.159321779922
 
0.2%
-0.266707193120
 
0.2%
-0.14924213519
 
0.2%
0.0876295184318
 
0.2%
0.583803859818
 
0.2%
-0.668074656117
 
0.2%
Other values (6618)10092
97.8%
ValueCountFrequency (%)
-0.69466758771
 
< 0.1%
-0.69387287122
< 0.1%
-0.69367667371
 
< 0.1%
-0.69340982181
 
< 0.1%
-0.69300574843
< 0.1%
-0.69295961871
 
< 0.1%
-0.69293100661
 
< 0.1%
-0.69231730551
 
< 0.1%
-0.69223847621
 
< 0.1%
-0.69190856081
 
< 0.1%
ValueCountFrequency (%)
16.218748121
 
< 0.1%
13.401566811
 
< 0.1%
10.669760582
 
< 0.1%
8.7626090981
 
< 0.1%
8.74804318813
0.1%
8.202417451
 
< 0.1%
7.880207491
 
< 0.1%
7.8734451011
 
< 0.1%
7.4773416861
 
< 0.1%
7.0650309594
 
< 0.1%

Line Item Insurance (USD)
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6936
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-0.4795104774
Maximum15.08883688
Zeros0
Zeros (%)0.0%
Negative7675
Negative (%)74.3%
Memory size80.8 KiB
2022-05-21T17:29:51.686150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.4795104774
5-th percentile-0.4789853678
Q1-0.46602425
median-0.383910242
Q30.02536722052
95-th percentile1.675204919
Maximum15.08883688
Range15.56834736
Interquartile range (IQR)0.4913914705

Descriptive statistics

Standard deviation1.000048434
Coefficient of variation (CV)nan
Kurtosis35.51248453
Mean0
Median Absolute Deviation (MAD)0.09391382577
Skewness4.862276887
Sum-5.684341886 × 10-14
Variance1.000096871
MonotonicityNot monotonic
2022-05-21T17:29:51.817795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.479490280841
 
0.4%
-0.479510477436
 
0.3%
-0.479389298233
 
0.3%
-0.479429691331
 
0.3%
-0.479409494830
 
0.3%
-0.479369101725
 
0.2%
-0.479470084323
 
0.2%
-0.479348905221
 
0.2%
-0.479288315619
 
0.2%
-0.478541044319
 
0.2%
Other values (6926)10046
97.3%
ValueCountFrequency (%)
-0.479510477436
0.3%
-0.47950037912
 
< 0.1%
-0.479490280841
0.4%
-0.479470084323
0.2%
-0.479449887814
 
0.1%
-0.479429691331
0.3%
-0.479409494830
0.3%
-0.479389298233
0.3%
-0.479369101725
0.2%
-0.47935900351
 
< 0.1%
ValueCountFrequency (%)
15.088836881
< 0.1%
13.669122391
< 0.1%
11.4974511
< 0.1%
10.776616951
< 0.1%
10.58640611
< 0.1%
10.192392161
< 0.1%
10.084886081
< 0.1%
9.9464995121
< 0.1%
9.9115797262
< 0.1%
9.816858041
< 0.1%

Managed By_Ethiopia Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10323 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Length

2022-05-21T17:29:51.939469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.002304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_Haiti Field Office
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10323 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Length

2022-05-21T17:29:52.180829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.243658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010323
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_PMO - US
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
10265 
0.0
 
59

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010265
99.4%
0.059
 
0.6%

Length

2022-05-21T17:29:52.305494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.368321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010265
99.4%
0.059
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Managed By_South Africa Field Office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10267 
1.0
 
57

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010267
99.4%
1.057
 
0.6%

Length

2022-05-21T17:29:52.432151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.494983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010267
99.4%
1.057
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_Direct Drop
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
5404 
1.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05404
52.3%
1.04920
47.7%

Length

2022-05-21T17:29:52.557814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.620651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.05404
52.3%
1.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fulfill Via_From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
5404 
0.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Length

2022-05-21T17:29:52.683481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.747311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10321 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010321
> 99.9%
1.03
 
< 0.1%

Length

2022-05-21T17:29:52.810140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.872971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010321
> 99.9%
1.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10049 
1.0
 
275

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010049
97.3%
1.0275
 
2.7%

Length

2022-05-21T17:29:52.935806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:52.998635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010049
97.3%
1.0275
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10315 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010315
99.9%
1.09
 
0.1%

Length

2022-05-21T17:29:53.061467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.124299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010315
99.9%
1.09
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_DDP
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8881 
1.0
1443 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08881
86.0%
1.01443
 
14.0%

Length

2022-05-21T17:29:53.188129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.250960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08881
86.0%
1.01443
 
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10309 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010309
99.9%
1.015
 
0.1%

Length

2022-05-21T17:29:53.313794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.376625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010309
99.9%
1.015
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_EXW
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7546 
1.0
2778 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.07546
73.1%
1.02778
 
26.9%

Length

2022-05-21T17:29:53.439455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.503284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07546
73.1%
1.02778
 
26.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9927 
1.0
 
397

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09927
96.2%
1.0397
 
3.8%

Length

2022-05-21T17:29:53.566120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.629946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09927
96.2%
1.0397
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Vendor INCO Term_N/A - From RDC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
5404 
0.0
4920 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Length

2022-05-21T17:29:53.692779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.755610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.05404
52.3%
0.04920
47.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Air
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
6113 
0.0
4211 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06113
59.2%
0.04211
40.8%

Length

2022-05-21T17:29:53.818442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:53.882272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06113
59.2%
0.04211
40.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9674 
1.0
 
650

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09674
93.7%
1.0650
 
6.3%

Length

2022-05-21T17:29:53.945102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.007937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09674
93.7%
1.0650
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
9953 
1.0
 
371

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.09953
96.4%
1.0371
 
3.6%

Length

2022-05-21T17:29:54.188454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.251246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09953
96.4%
1.0371
 
3.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Shipment Mode_Truck
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7494 
1.0
2830 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07494
72.6%
1.02830
 
27.4%

Length

2022-05-21T17:29:54.314115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.376949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07494
72.6%
1.02830
 
27.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10308 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Length

2022-05-21T17:29:54.440777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.504605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ANTM
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10302 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010302
99.8%
1.022
 
0.2%

Length

2022-05-21T17:29:54.567437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.630272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010302
99.8%
1.022
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_ARV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
8550 
0.0
1774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.08550
82.8%
0.01774
 
17.2%

Length

2022-05-21T17:29:54.693102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.755932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.08550
82.8%
0.01774
 
17.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_HRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8596 
1.0
1728 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08596
83.3%
1.01728
 
16.7%

Length

2022-05-21T17:29:54.818764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:54.881596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08596
83.3%
1.01728
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Product Group_MRDT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10316 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010316
99.9%
1.08
 
0.1%

Length

2022-05-21T17:29:54.945427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.009218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010316
99.9%
1.08
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_ACT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10308 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Length

2022-05-21T17:29:55.085015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.161809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010308
99.8%
1.016
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Adult
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
6595 
0.0
3729 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06595
63.9%
0.03729
36.1%

Length

2022-05-21T17:29:55.228671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.292500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.06595
63.9%
0.03729
36.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_HIV test
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8757 
1.0
1567 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08757
84.8%
1.01567
 
15.2%

Length

2022-05-21T17:29:55.356289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.433084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08757
84.8%
1.01567
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10163 
1.0
 
161

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010163
98.4%
1.0161
 
1.6%

Length

2022-05-21T17:29:55.509878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.575742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010163
98.4%
1.0161
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Malaria
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
10294 
1.0
 
30

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.010294
99.7%
1.030
 
0.3%

Length

2022-05-21T17:29:55.638571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.701403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.010294
99.7%
1.030
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sub Classification_Pediatric
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
8369 
1.0
1955 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.08369
81.1%
1.01955
 
18.9%

Length

2022-05-21T17:29:55.764235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.838001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08369
81.1%
1.01955
 
18.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_No
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
0.0
7030 
1.0
3294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07030
68.1%
1.03294
31.9%

Length

2022-05-21T17:29:55.914794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:55.982614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.07030
68.1%
1.03294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

First Line Designation_Yes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
1.0
7030 
0.0
3294 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07030
68.1%
0.03294
31.9%

Length

2022-05-21T17:29:56.045445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-21T17:29:56.108314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.07030
68.1%
0.03294
31.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-05-21T17:29:45.966415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:38.137669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.707460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.712777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.857067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.831457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.826790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.844667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.095110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:38.398960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.831090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.838440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.976748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.953137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.949430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.088796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.223729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:38.661258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.960788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.965103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.099412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.076803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.078119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.213431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.352383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:38.930537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.086453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.096789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.224079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.202429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.206786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.341127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.475086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.182863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.210163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.221416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.341764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.323109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.330451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.463760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.601716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.331427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.334829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.348076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.463407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.448812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.464092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.589425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.729373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.456093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.463443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.477729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.586095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.575464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.589717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.716129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:46.857033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:39.578803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:40.587113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:41.603396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:42.705761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:43.700134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:44.717010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-21T17:29:45.839754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-21T17:29:56.346639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-21T17:29:56.851326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-21T17:29:57.365948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-21T17:29:57.869563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-21T17:29:58.290439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-21T17:29:47.121365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-21T17:29:48.806860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
0-0.626709-0.457457-0.4550850.1547910.108859-0.340033-0.649146-0.4779860.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.0
12.115663-0.432952-0.438724-0.345104-0.178113-0.312457-0.430691-0.4584460.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
20.287415-0.445442-0.3408281.2729780.056960-0.327404-0.598144-0.3510810.00.01.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.0
3-0.2349420.339404-0.087803-0.393559-0.165902-0.1928020.239975-0.0623920.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
4-0.2349420.491277-0.104488-0.410880-0.1720080.2655961.959214-0.0567870.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
52.115663-0.447540-0.450235-0.363741-0.181166-0.300787-0.349006-0.4721790.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
60.156826-0.454559-0.4440130.229337-0.077368-0.314855-0.605485-0.4703610.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.00.00.00.00.01.00.01.0
7-0.234942-0.041604-0.280485-0.401014-0.168955-0.222935-0.331956-0.3562310.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
8-0.234942-0.451112-0.455139-0.438287-0.178113-0.302785-0.410859-0.4782680.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
90.548593-0.387990-0.1233720.420087-0.083474-0.289677-0.130211-0.1071670.00.01.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0

Last rows

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes
10314-0.234942-0.199647-0.348868-0.402110-0.1689550.1620870.294509-0.4022390.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.01.00.0
103150.5485931.2906103.322444-0.072354-0.1384260.8737020.8339942.2294890.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10316-0.234942-0.083244-0.173421-0.338088-0.153690-0.217420-0.495594-0.2470490.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10317-0.626709-0.289972-0.395920-0.412634-0.1567430.1531342.066158-0.4296650.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10318-0.2349424.6688671.683340-0.402110-0.1689551.7275121.9978391.2768800.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
10319-0.2349423.7028731.280116-0.402110-0.1689551.7275121.9978390.9459200.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0
10320-0.2349420.068430-0.058757-0.338088-0.1536900.0126980.165692-0.1529330.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10321-0.62670912.39449814.430762-0.262008-0.0865274.6651333.19273010.1923920.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.01.00.0
10322-0.234942-0.021670-0.126871-0.338088-0.153690-0.229809-0.480272-0.2088370.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.0
10323-0.2349420.457280-0.245505-0.437410-0.1781130.709366-0.013212-0.3062040.00.01.00.00.01.00.00.00.00.00.00.00.01.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.0

Duplicate rows

Most frequently occurring

Unit of Measure (Per Pack)Line Item QuantityLine Item ValuePack PriceUnit PriceWeight (Kilograms)Freight Cost (USD)Line Item Insurance (USD)Managed By_Ethiopia Field OfficeManaged By_Haiti Field OfficeManaged By_PMO - USManaged By_South Africa Field OfficeFulfill Via_Direct DropFulfill Via_From RDCVendor INCO Term_CIFVendor INCO Term_CIPVendor INCO Term_DAPVendor INCO Term_DDPVendor INCO Term_DDUVendor INCO Term_EXWVendor INCO Term_FCAVendor INCO Term_N/A - From RDCShipment Mode_AirShipment Mode_Air CharterShipment Mode_OceanShipment Mode_TruckProduct Group_ACTProduct Group_ANTMProduct Group_ARVProduct Group_HRDTProduct Group_MRDTSub Classification_ACTSub Classification_AdultSub Classification_HIV testSub Classification_HIV test - AncillarySub Classification_MalariaSub Classification_PediatricFirst Line Designation_NoFirst Line Designation_Yes# duplicates
800.287415-0.3893890.2506481.4703050.084436-0.2462750.7608400.2119780.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.06
4-1.005417-0.383294-0.2446530.0561287.292324-0.1818510.839035-0.2966310.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.05
992.899197-0.457881-0.4565450.033106-0.162849-0.300347-0.159322-0.4794090.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.00.01.00.01.05
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2-1.005417-0.456807-0.456030-0.3714151.339176-0.337954-0.611779-0.4790060.00.01.00.01.00.00.00.00.00.00.01.00.00.01.00.00.00.00.00.00.01.00.00.00.00.01.00.00.00.01.03
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